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145 lines
7.4 KiB
HTML
145 lines
7.4 KiB
HTML
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<meta name="description" content="Miller documentation"/>
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<title> How original is Miller? </title>
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The background image is from a screenshot of a Google search for "data analysis
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tools", lightened and sepia-toned. Over this was placed a Mac Terminal app with
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very light-grey font and translucent background, in which a few statistical
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Miller commands were run with pretty-print-tabular output format.
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<div class="pokinav">
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<center><titleinbody>Miller</titleinbody></center>
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<!-- PAGE LIST GENERATED FROM template.html BY poki -->
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<br/>User info:
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<br/>• <a href="index.html">About</a>
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<br/>• <a href="file-formats.html">File formats</a>
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<br/>• <a href="feature-comparison.html">Miller features in the context of the Unix toolkit</a>
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<br/>• <a href="record-heterogeneity.html">Record-heterogeneity</a>
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<br/>• <a href="performance.html">Performance</a>
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<br/>• <a href="etymology.html">Why call it Miller?</a>
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<br/>• <a href="originality.html">How original is Miller?</a>
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<br/>• <a href="reference.html">Reference</a>
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<br/>• <a href="data-examples.html">Data examples</a>
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<br/>• <a href="to-do.html">Things to do</a>
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<br/>Developer info:
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<br/>• <a href="build.html">Compiling, portability, dependencies, and testing</a>
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<br/>• <a href="whyc.html">Why C?</a>
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<br/>• <a href="contact.html">Contact information</a>
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<br/>• <a href="https://github.com/johnkerl/miller">GitHub repo</a>
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<br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/>
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<br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/>
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<br/> <br/> <br/> <br/> <br/> <br/>
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<td>
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<center> <titleinbody> How original is Miller? </titleinbody> </center>
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<p>
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<!-- BODY COPIED FROM content-for-originality.html BY poki -->
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<p/> It isn’t. Miller is one of many, many participants in the
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online-analytical-processing culture. Other key participants include
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<tt>awk</tt>, SQL, spreadsheets, etc. etc. etc. Far from being an original
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concept, Miller explicitly strives to imitate several existing tools:
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<p/>
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<boldmaroon>Unix toolkit</boldmaroon>: Intentional similarities as described in
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<a href="feature-comparison.html">Miller features in the context of the Unix toolkit</a>.
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<p/>Recipes abound for command-line data analysis using the Unix toolkit. Here are just a couple of my favorites:
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<ul>
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<li/> <a href="http://en.wikibooks.org/wiki/Ad_Hoc_Data_Analysis_From_The_Unix_Command_Line">http://en.wikibooks.org/wiki/Ad_Hoc_Data_Analysis_From_The_Unix_Command_Line</a>
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<li/> <a href="http://www.gregreda.com/2013/07/15/unix-commands-for-data-science">http://www.gregreda.com/2013/07/15/unix-commands-for-data-science</a>
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</ul>
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<p/> <boldmaroon>RecordStream</boldmaroon>: Miller owes particular inspiration
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to <a href="https://github.com/benbernard/RecordStream">RecordStream</a>. The
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key difference is that RecordStream is a Perl-based tool for manipulating JSON
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(including requiring it to separately manipulate other formats such as CSV into
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and out of JSON), while Miller is fast C which handles its formats natively.
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The similarities include the <tt>sort</tt>, <tt>stats1</tt> (analog of
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RecordStream’s <tt>collate</tt>), and <tt>delta</tt> operations, as well
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as <tt>filter</tt> and <tt>put</tt>, and pretty-print formatting.
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<p/> <boldmaroon>stats_m</boldmaroon>: A third source of lineage is my Python
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<a href="https://github.com/johnkerl/scripts-math/tree/master/stats">stats_m</a>
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module. This includes simple single-pass algorithms which form Miller’s
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<tt>stats1</tt> and <tt>stats2</tt> subcommands.
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<p/> <boldmaroon>SQL</boldmaroon>: Fourthly, Miller’s <tt>group-by</tt> command
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name is from SQL, as is the term <tt>aggregate</tt>.
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<p/> <boldmaroon>Added value</boldmaroon>:
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Miller’s added values include:
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<ul>
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<li> Name-indexing, compared to the Unix toolkit’s positional indexing.
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<li> Raw speed, compared to <tt>awk</tt>, RecordStream, <tt>stats_m</tt>, or various other kinds of Python/Ruby/etc. scripts one can easily create.
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<li> Ability to handle text files on the Unix pipe, without need for creating database tables, compared to SQL databases.
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</ul>
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<p/><boldmaroon>jq</boldmaroon>: Miller does for name-indexed text what <a
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href="http://stedolan.github.io/jq/">jq</a> does for JSON. If you’re not
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already familiar with <tt>jq</tt>, please check it out!
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<p/><boldmaroon>What about DOTADIW?</boldmaroon> One of the key points of the
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<a href="http://en.wikipedia.org/wiki/Unix_philosophy">Unix philosophy</a> is
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that a tool should do one thing and do it well. Hence <tt>sort</tt> and
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<tt>cut</tt> do just one thing. Why does Miller put <tt>awk</tt>-like
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processing, a few SQL-like operations, and statistical reduction all into one
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tool (see also <a href="reference.html">Reference</a>)? This is a fair
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question. First note that many standard tools, such as <tt>awk</tt> and
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<tt>perl</tt>, do quite a few things — as does <tt>jq</tt>. But I could
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have pushed for putting format awareness and name-indexing options into
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<tt>cut</tt>, <tt>awk</tt>, and so on (so you could do <tt>cut -f
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hostname,uptime</tt> or <tt>awk '{sum += $x*$y}END{print sum}'</tt>). Patching
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<tt>cut</tt>, <tt>sort</tt>, etc. on multiple operating systems is a
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non-starter in terms of uptake. Moreover, it makes sense for me to have Miller
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be a tool which collects together format-aware record-stream processing into
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one place, with good reuse of Miller-internal library code.
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<p/> <boldmaroon>No, really, why one more command-line data-manipulation
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tool?</boldmaroon> I wrote Miller because I was frustrated with tools like
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<tt>grep</tt>, <tt>sed</tt>, and so on being <i>line-aware</i> without being
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<i>format-aware</i>. The single most poignant example I can think of is seeing
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people grep data lines out of their CSV files and sadly losing their header
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lines. While some lighter-than-SQL processing is very nice to have, at core I
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wanted the format-awareness of <a
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href="https://github.com/benbernard/RecordStream">RecordStream</a> combined
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with the raw speed of the Unix toolkit. Miller does precisely that.
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</td>
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</table>
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</body>
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</html>
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